 Potz Donnerwetter! Hauptmann, Gerhart: Der Biberpelz. Berlin, 1893.
`Man alive!'

This narrows down Donnerwetter's contextual distribution due to our model only considering words within a sentence as context. H2 and frequency are not sensitive to this and rank the word much higher. This shows that, (i), different factors play a role in determining the contextual distribution of a word suggesting that a model of semantic change should incorporate different types of information and, (ii), that H2 and frequency may still be helpful in detecting metaphoric change in certain settings. The dominance of H may also be a hint to this direction: Word entropy combines frequency and contextual distribution as it is influenced by both.
Feder and Haube from the very bottom of the gold rank are not beyond the bottom-items of any measure's prediction. In H's prediction, which is the best-performing measure, they rank near the middle (12, 18). This indicates that their position at the bottom of the gold rank may not accurately reflect the semantic change they underwent. Similarly for the adjectives freundlich and fett ranking in all predictions near middle or lower (for H: 18, 10). We still have to assess how these words behave in future studies.
<TABLE>Conclusion
Semantic generality is an important indicator of semantic change. As [1]} puts it, generalization and specialization are two basic principles of meaning change. We proposed a way to detect metaphoric change based on semantic generality and built a test set for the evaluation of computational models of metaphoric change in German. We proposed an annotation procedure strictly derived from comparable synchronic work and showed that annotators can show reasonable agreement. Different distributional measures based on the information-theoretic concept of entropy were compared against the annotators judgments and it was found that raw word entropy correlates strongly and significantly with the gold rank in different settings in contrast to most other entropy measures and frequency. We found evidence that HMON predicts well with certain parameter settings.
Both, the annotation procedure and the computational model, are generalizable to different types of semantic change. Moreover, our model is unsupervised and language-independent as it relies, in principle, on minimal linguistic input, since entropy can be computed already from a raw token co-occurrence matrix. Yet, the model profits from richer input as indicated in [2]}.
Future studies should test how well word entropy distinguishes metaphoric change from other types of meaning innovation and how well it detects innovative and reductive meaning change in general. The latter may be tested straightforwardly on the English data of [3]} and [4]}. In doing so, it will be interesting to see how our model performs in comparison to diachronic similarity and WSI models.

Acknowledgments
We thank Prof. Dr. Sebastian Padó (Institute for Natural Language Processing, University of Stuttgart) for pointing out his idea to normalize word entropy via OLS. We are very grateful to Prof. Dr. Olav Hackstein (Historical and Indo-European Linguistics, LMU Munich) and his research colloquium for valuable discussions and comments and to Sascha Schlechtweg for statistical advice. We would like to thank Andrew Wigman for careful proof-reading as well as Jörg Förstner, Michael Frotscher, Altina Mujkic, Edona Neziri, Cornelia van Scherpenberg, Christian Soetebier and Veronika Vasileva for help related to the annotation process. Last but not least, we thank the reviewers for constructive criticism helping us to improve the paper substantially.

Hyperparameters and Corpus Preprocessing Details
Hyperparameters
Second-order word entropy has 3 hyperparameters: (i), the number of positively associated contexts \(N\)  to compute the average/median from; (ii), whether to use median or average entropy among the top \(N\)  contexts;Note that for any test pair, \(N\)  is the maximal number of associated contexts, which is reduced to \(M\)  if a test target has only \(M\)  (\(<N\) ) positively associated contexts in one of the two matrices to compare. and (iii), the association metric used to sort the contexts by relevance (i.e., PPMI or PLMI). We choose the following combination of hyperparameters: \(\langle \text{100, median, PLMI} \rangle \) , which is suggested by the work of [2]}.
For MON entropy normalization we choose \(n = 29\) , because that is the lowest context number of a word in one of its two relevant time periods, and \(k = 10000\) . For OLS normalization we choose \(n = 1000\) .

Corpus Preprocessing
Words that occur less than 5 times in the whole corpus, functional words and punctuation are deleted. As functional words we regard those which are not tagged with a POS-tag starting with either `N', `V' or `AD'. Every token is then replaced by its lemma form combined with the starting of its POS-tag, e.g., geht is replaced by gehen:V. Note that both diachronic lemmatization and POS-tagging are provided by DTA.
<TABLE>
Annotation Guidelines
Introduction
Following the terminology from [6]} innovative meaning change, as opposed to reductive meaning change, is where the existing meaning \(M_A\)  of a word acquires a new meaning \(M_B\) , where this normally happens over a long period of time.
Metaphoric Change
is, then, a subcategory of innovative meaning change (besides metonymic change, generalization...) where \(M_B\)  is related to \(M_A\)  by similarity or a reduced comparison [6]}. [8]}

Note that the annotation process described below is a combination and modification of the processes described by [9]}, [8]} and [11]}.

Annotation Process
You will be given an OpenOffice table document with approximately 560 lines. In every line you will see in columns 2 and 3 two uses of a word (the target word contained in column 1) with its surrounding contexts. The relevant word is marked in bold font in both contexts.

For each such use of a word establish its meaning in context, that is, how it applies to an entity, relation, or attribute in the situation evoked by the text (contextual meaning). Take into account what comes before and after the word. Note that the word might be used differently from what you are familiar with. Don't let yourself be confused by alternative spelling.

Try to find an interpretation where the meaning in the second context (M2) is related to the meaning
in the first context (M1) in one or more of the following ways:

M2 is less concrete than M1 (what it evokes is harder to imagine, see, hear, feel, smell, and taste);

M2 is less human-oriented than M1;

M2 is not related to bodily action in contrast to M1;

M2 is less precise than M1 (precise as opposed to vague).


If M2 is indeed related to M1 in one or more of these ways, decide whether M2 contrasts with M1 but can be
understood in comparison with it. (See below for an example.)



If yes, fill in 1 into the column headed by `M2 is metaphorically related to M1', judging M2 as being metaphorically related to M1.

If no, fill in 0 into the column headed by `M2 is is metaphorically related to M1', judging M2 as not being metaphorically related to M1.

If you cannot decide, e.g., because the word marked in bold font doesn't match the word shown in column 1 in meaning or part of speech, you don't understand either of the contexts, one is too unspecific or other reasons, don't perform evaluation, fill in a 1 into the comments column and go on to the next test item.


Compare the two meanings in the other direction, i.e., decide whether M1 is metaphorically related to M2 by going through steps 2 to 4 and fill your judgment into the column
headed by `M1 is metaphoric compared to M2'.

Please make sure that you don't change anything in the file apart from column width, your judgments and comments. Finally, return the annotated document to the above-mentioned email address. If you have any further questions on the task, don't hesitate to ask.

Annotation Example
The following example illustrates how the procedure operates in practice. Consider Table REF  as an example table similar to the one you will receive for annotation.
belowskip=-17pt
<TABLE>In line 1 you need to compare two uses of the word umwälzen. In context 1 umwälzen is used in the sense `to turn around something or someone physically' (M1). This contrasts with its use in context 2 where it is used in the sense `to change something radically' (M2). M2 is clearly less concrete than M1 and not necessarily related to bodily action. Moreover, M2 is less precise, since we may have greater disagreement about the question whether something `changed radically' than we may have on the question whether someone or something (was) turned around. (You may have a different intuition here, which should then be reflected in your judgment accordingly.)
Now, as we saw, M2 contrasts with M1. However, it can be understood in comparison with it: We can understand abstract change in terms of physical or local change. Consequently, we fill in 1 in the column headed by `M2 is metaphorically related to M1', judging M2 to be metaphorically related to M1.
And, for the same reasons as mentioned above, we fill in a 0 in the column headed by `M1 is metaphorically related to M2'.
In line 2 both meanings of umwälzen, M1 and M2, are similarly concrete, human-oriented, related to bodily action and precise. They don't contrast with each other. (You may want to say that they are equal.) Hence, neither meaning has a metaphoric relation to the other. Consequently, we fill in 0 into both columns.
 

Metaphoric Change Test Set
<TABLE>